ControlNet

ControlNet

Adding Conditional Control to Text-to-Image Diffusion Models (ControlNet) by Lvmin Zhang and Maneesh Agrawala.

This example is based on the training example in the original ControlNet repository. It trains a ControlNet to fill circles using a small synthetic dataset.

Installing the dependencies

Before running the scripts, make sure to install the library’s training dependencies.

To successfully run the latest versions of the example scripts, we highly recommend installing from source and keeping the installation up to date. We update the example scripts frequently and install example-specific requirements.

To do this, execute the following steps in a new virtual environment:

Copied

git clone https://github.com/boincai/diffusers
cd diffusers
pip install -e .

Then navigate into the example folder

Copied

cd examples/controlnet

Now run:

Copied

pip install -r requirements.txt

And initialize an 🌍 Accelerate environment with:

Copied

accelerate config

Or for a default 🌍 Accelerate configuration without answering questions about your environment:

Copied

accelerate config default

Or if your environment doesn’t support an interactive shell like a notebook:

Copied

from accelerate.utils import write_basic_config

write_basic_config()

Circle filling dataset

The original dataset is hosted in the ControlNet repo, but we re-uploaded it here to be compatible with 🌍 Datasets so that it can handle the data loading within the training script.

Our training examples use runwayml/stable-diffusion-v1-5 because that is what the original set of ControlNet models was trained on. However, ControlNet can be trained to augment any compatible Stable Diffusion model (such as CompVis/stable-diffusion-v1-4) or stabilityai/stable-diffusion-2-1.

To use your own dataset, take a look at the Create a dataset for training guide.

Training

Download the following images to condition our training with:

Copied

wget https://boincai.com/datasets/boincai/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png

wget https://boincai.com/datasets/boincai/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png

Specify the MODEL_NAME environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the pretrained_model_name_or_path argument.

The training script creates and saves a diffusion_pytorch_model.bin file in your repository.

Copied

export MODEL_DIR="runwayml/stable-diffusion-v1-5"
export OUTPUT_DIR="path to save model"

accelerate launch train_controlnet.py \
 --pretrained_model_name_or_path=$MODEL_DIR \
 --output_dir=$OUTPUT_DIR \
 --dataset_name=fusing/fill50k \
 --resolution=512 \
 --learning_rate=1e-5 \
 --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
 --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
 --train_batch_size=4 \
 --push_to_hub

This default configuration requires ~38GB VRAM.

By default, the training script logs outputs to tensorboard. Pass --report_to wandb to use Weights & Biases.

Gradient accumulation with a smaller batch size can be used to reduce training requirements to ~20 GB VRAM.

Copied

export MODEL_DIR="runwayml/stable-diffusion-v1-5"
export OUTPUT_DIR="path to save model"

accelerate launch train_controlnet.py \
 --pretrained_model_name_or_path=$MODEL_DIR \
 --output_dir=$OUTPUT_DIR \
 --dataset_name=fusing/fill50k \
 --resolution=512 \
 --learning_rate=1e-5 \
 --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
 --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
 --train_batch_size=1 \
 --gradient_accumulation_steps=4 \
  --push_to_hub

Training with multiple GPUs

accelerate allows for seamless multi-GPU training. Follow the instructions here for running distributed training with accelerate. Here is an example command:

Copied

export MODEL_DIR="runwayml/stable-diffusion-v1-5"
export OUTPUT_DIR="path to save model"

accelerate launch --mixed_precision="fp16" --multi_gpu train_controlnet.py \
 --pretrained_model_name_or_path=$MODEL_DIR \
 --output_dir=$OUTPUT_DIR \
 --dataset_name=fusing/fill50k \
 --resolution=512 \
 --learning_rate=1e-5 \
 --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
 --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
 --train_batch_size=4 \
 --mixed_precision="fp16" \
 --tracker_project_name="controlnet-demo" \
 --report_to=wandb \
  --push_to_hub

Example results

After 300 steps with batch size 8

red circle with blue background

cyan circle with brown floral background

After 6000 steps with batch size 8:

red circle with blue background

cyan circle with brown floral background

Training on a 16 GB GPU

Enable the following optimizations to train on a 16GB GPU:

Now you can launch the training script:

Copied

export MODEL_DIR="runwayml/stable-diffusion-v1-5"
export OUTPUT_DIR="path to save model"

accelerate launch train_controlnet.py \
 --pretrained_model_name_or_path=$MODEL_DIR \
 --output_dir=$OUTPUT_DIR \
 --dataset_name=fusing/fill50k \
 --resolution=512 \
 --learning_rate=1e-5 \
 --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
 --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
 --train_batch_size=1 \
 --gradient_accumulation_steps=4 \
 --gradient_checkpointing \
 --use_8bit_adam \
  --push_to_hub

Training on a 12 GB GPU

Enable the following optimizations to train on a 12GB GPU:

Copied

export MODEL_DIR="runwayml/stable-diffusion-v1-5"
export OUTPUT_DIR="path to save model"

accelerate launch train_controlnet.py \
 --pretrained_model_name_or_path=$MODEL_DIR \
 --output_dir=$OUTPUT_DIR \
 --dataset_name=fusing/fill50k \
 --resolution=512 \
 --learning_rate=1e-5 \
 --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
 --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
 --train_batch_size=1 \
 --gradient_accumulation_steps=4 \
 --gradient_checkpointing \
 --use_8bit_adam \
 --enable_xformers_memory_efficient_attention \
 --set_grads_to_none \
  --push_to_hub

When using enable_xformers_memory_efficient_attention, please make sure to install xformers by pip install xformers.

Training on an 8 GB GPU

We have not exhaustively tested DeepSpeed support for ControlNet. While the configuration does save memory, we have not confirmed whether the configuration trains successfully. You will very likely have to make changes to the config to have a successful training run.

Enable the following optimizations to train on a 8GB GPU:

DeepSpeed can offload tensors from VRAM to either CPU or NVME. This requires significantly more RAM (about 25 GB).

You’ll have to configure your environment with accelerate config to enable DeepSpeed stage 2.

The configuration file should look like this:

Copied

compute_environment: LOCAL_MACHINE
deepspeed_config:
  gradient_accumulation_steps: 4
  offload_optimizer_device: cpu
  offload_param_device: cpu
  zero3_init_flag: false
  zero_stage: 2
distributed_type: DEEPSPEED

See documentation for more DeepSpeed configuration options.

Changing the default Adam optimizer to DeepSpeed’s Adam deepspeed.ops.adam.DeepSpeedCPUAdam gives a substantial speedup but it requires a CUDA toolchain with the same version as PyTorch. 8-bit optimizer does not seem to be compatible with DeepSpeed at the moment.

Copied

export MODEL_DIR="runwayml/stable-diffusion-v1-5"
export OUTPUT_DIR="path to save model"

accelerate launch train_controlnet.py \
 --pretrained_model_name_or_path=$MODEL_DIR \
 --output_dir=$OUTPUT_DIR \
 --dataset_name=fusing/fill50k \
 --resolution=512 \
 --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
 --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
 --train_batch_size=1 \
 --gradient_accumulation_steps=4 \
 --gradient_checkpointing \
 --enable_xformers_memory_efficient_attention \
 --set_grads_to_none \
 --mixed_precision fp16 \
 --push_to_hub

Inference

The trained model can be run with the StableDiffusionControlNetPipeline. Set base_model_path and controlnet_path to the values --pretrained_model_name_or_path and --output_dir were respectively set to in the training script.

Copied

from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image
import torch

base_model_path = "path to model"
controlnet_path = "path to controlnet"

controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    base_model_path, controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
)

# speed up diffusion process with faster scheduler and memory optimization
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# remove following line if xformers is not installed
pipe.enable_xformers_memory_efficient_attention()

pipe.enable_model_cpu_offload()

control_image = load_image("./conditioning_image_1.png")
prompt = "pale golden rod circle with old lace background"

# generate image
generator = torch.manual_seed(0)
image = pipe(prompt, num_inference_steps=20, generator=generator, image=control_image).images[0]

image.save("./output.png")

Stable Diffusion XL

Training with Stable Diffusion XL is also supported via the train_controlnet_sdxl.py script. Please refer to the docs here.

PreviousLow-Rank Adaptation of Large Language Models (LoRA)NextInstructPix2Pix Training

Last updated